Methods and systems for managing service level objectives in a networked storage environment

ABSTRACT

Methods and systems for a networked storage system are provided. One method includes assigning by a processor executable management module a service level objective (SLO) for a workload, where the SLO is allotted a plurality of performance parameters for tracking performance of the workload for storing data in a networked storage environment; tracking historical performance of the workload to determine a duration when SLO allotment defined by the plurality of performance parameters is being under-utilized; adjusting automatically the SLO allotment for the workload during the duration when the SLO allotment is under-utilized; and re-allocating automatically the available performance capacity of a resource used by the workload to another workload whose assigned SLO is not being under-utilized.

TECHNICAL FIELD The present disclosure relates to managing service levelobjectives in a networked storage environment. BACKGROUND

Various forms of storage systems are used today. These forms includedirect attached storage (DAS) network attached storage (NAS) systems,storage area networks (SANs), and others. Network storage systems arecommonly used for a variety of purposes, such as providing multipleclients with access to shared data, backing up data and others.

A storage system typically includes at least a computing systemexecuting a storage operating system for storing and retrieving data onbehalf of one or more client computing systems (may just be referred toas “client” or “clients”). The storage operating system stores andmanages shared data containers in a set of mass storage devices.

Quality of Service (QOS) is a metric used in a networked storageenvironment to provide certain throughput for processing input/output(I/O) requests for reading or writing data, a response time goal within,which I/O requests are processed and a number of I/O requests processedwithin a given time (for example, in a second (IOPS). Throughput meansamount of data transferred within a given time, for example, inmegabytes per second (Mb/s).

Clients today may be provided with a guaranteed service level when itcomes to storage performance. The service level is typically defined bya service level object that is associated with a particular storagelevel and defines a service level objective (SLO). SLOB are typicallyassociated with a workload (for example, a volume, LUN, file andothers). The workload is assigned to one or more resources for storingdata and retrieving data for the workload. The resource utilization andworkload performance vary in different periods. Conventional systems donot provide efficient tools for sharing SLO allotments based on SLOtracking. Continuous efforts are being made to provide bettermanageability solutions for networked storage environments that are usedby various clients.

BRIEF DESCRIPTION OF THE DRAWINGS

The various features of the present disclosure will now be describedwith reference to the drawings of the various aspects. In the drawings,the same components may have the same reference numerals. Theillustrated aspects are intended to illustrate, but not to limit thepresent disclosure. The drawings include the following Figures:

FIG. 1 shows an example of an operating environment for the variousaspects disclosed herein;

FIG. 2A shows an example of a clustered storage system, used accordingto one aspect of the present disclosure;

FIG. 2B shows an example of a performance manager, according to oneaspect of the present disclosure;

FIG. 2C shows an example of handling QOS requests by a storage system,according to one aspect of the present disclosure;

FIG. 2D shows an example of a resource layout used by the performancemanager, according to one aspect of the present disclosure;

FIG. 2E shows an example of managing workloads and resources by theperformance manager, according to one aspect of the present disclosure;

FIG. 3A shows a format for managing various resource objects, accordingto one aspect of the present disclosure;

FIG. 3B shows an example of certain counters that are used, according toone aspect of the present disclosure;

FIG. 4 shows an example of an overall process flow for SLO management,according to one aspect of the present disclosure;

FIG. 5 shows an example of a layout with various components for SLOmanagement, according to one aspect of the present disclosure;

FIG. 6A illustrates a historical view generated by the performancemanager for SLO management, according to one aspect of the presentdisclosure;

FIG. 6B illustrates a state based model for determining idle andnon-idle time for SLO management, according to one aspect of the presentdisclosure;

FIG. 6C shows an example of a latency v. utilization curve (LvU), fordetermining headroom (performance capacity), according to one aspect ofthe present disclosure;

FIG. 7 shows an example of a storage system, used according to oneaspect of the present disclosure;

FIG. 8 shows an example of a storage operating system, used according toone aspect of the present disclosure; and

FIG. 9 shows an example of a processing system, used according to oneaspect of the present disclosure.

DETAILED DESCRIPTION

As a preliminary note, the terms “component”, “module”, “system,” andthe like as used herein are intended to refer to a computer-relatedentity, either software-executing general purpose processor, hardware,firmware and a combination thereof. For example, a component may be, butis not limited to being, a process running on a hardware processor, ahardware based processor, an object, an executable, a thread ofexecution, a program, and/or a computer.

By way of illustration, both an application running on a server and theserver can be a component. One or more components may reside within aprocess and/or thread of execution, and a component may be localized onone computer and/or distributed between two or more computers. Also,these components can execute from various computer readable media havingvarious data structures stored thereon. The components may communicatevia local and/or remote processes such as in accordance with a signalhaving one or more data packets (e.g., data from one componentinteracting with another component in a local system, distributedsystem, and/or across a network such as the Internet with other systemsvia the signal).

Computer executable components can be stored, for example, atnon-transitory, computer readable media including, but not limited to,an ASIC (application specific integrated circuit), CD (compact disc),DVD (digital video disk), ROM (read only memory), floppy disk, harddisk, EEPROM (electrically erasable programmable read only memory),memory stick or any other storage device, in accordance with the claimedsubject matter.

In one aspect, a performance manager module is provided that interfaceswith a storage operating system to collect quality of service (QOS) data(or performance data) for various resources. QOS provides a certainthroughput (i.e.

amount of data that is transferred within a given time interval (forexample, megabytes per seconds (MBS)), latency and/or a number ofinput/output operations that can be processed within a time interval,for example, in a second (referred to as IOPS). Latency means a delay incompleting the processing of an I/O request and may be measured usingdifferent metrics for example, a response time in processing I/Orequests.

In one aspect, methods and systems for managing resources in a networkedstorage environment is provided. The resources may be managed based onremaining (or useful) performance capacity at any given time that isavailable for a resource relative to a peak/optimal performance capacitywithout violating any performance expectations. The availableperformance capacity may be referred to as “headroom” that is discussedin detail below. The resource maybe any resource in the networkedstorage environment, including processing nodes and aggregates that aredescribed below in detail. The type of performance capacity will varybased on the resource type, for example, the performance capacity of aCPU is the percentage of available CPU resources, the performancecapacity of a switch is the ability to transfer data at a certain rateand the performance capacity of a storage device is the capacity tostore data and return data. Peak performance capacity of a resource maybe determined according to performance limits that may be set bypolicies (for example, QoS or service level objectives (“SLOs”) asdescribed below).

In one aspect, a management tool is provided that enables a user toassociate different SLOs to different workloads (for example, volume,LUN and other logical objects). The workloads associated with the SLOsare not constant and change over time. Some workloads quiesce during acertain duration for one or more reasons (for example, time of the day,geographical location of the data centers and others). In one aspect,the management tool enables a storage administrator to automaticallychange SLO throughputs to accommodate shifts or changes in workloads, asdescribed below in detail. Historical performance and headroom data isused to identify durations when a given SLO is not utilizing some or allof its allotment. This information with the historical performance dataprovides an understanding of the relationship between SLOs and workloadswithin a storage network. This enables users to setup automaticrebalancing of SLO allotments, as described below in detail.

Before describing the various aspects of the present disclosure, thefollowing provides a description of the overall networked storageenvironment and the resources used in the operating environment forstoring data.

System 100: FIG. 1 shows an example of a system 100, where the variousadaptive aspects disclosed herein may be implemented. System 100includes a performance manager 121 that interfaces with a storageoperating system 107 of a storage system 108 for receiving QOS data. Theperformance manager 121 may be a processor executable module that isexecuted by one or more processors out of a memory device.

The performance manager 121 obtains the QOS data and stores it at a datastructure 125. The performance manager 121 also uses a SLO managementdata structure 125A for tracking SLO allotments and rebalancing SLOallotments as described below in detail. Details regarding the variousoperations performed by the performance manager 121 are provided below.

In one aspect, storage system 108 has access to a set of mass storagedevices 114A-114N (may be referred to as storage devices 114 or simplyas storage device 114) within at least one storage subsystem 112. Thestorage devices 114 may include writable storage device media such asmagnetic disks, video tape, optical, DVD, magnetic tape, non-volatilememory devices for example, solid state drives (SSDs) includingself-encrypting drives, flash memory devices and any other similar mediaadapted to store information. The storage devices 114 may be organizedas one or more groups of Redundant Array of Independent (or Inexpensive)Disks (RAID). The aspects disclosed are not limited to any particularstorage device type or storage device configuration.

In one aspect, the storage system 108 provides a set of logical storagevolumes (may be interchangeably referred to as volume or storage volume)for providing physical storage space to clients 116A-116N (or virtualmachines (VMs) 105A-105N). A storage volume is a logical storage objectand typically includes a file system in a NAS environment or a logicalunit number (LUN) in a SAN environment. The various aspects describedherein are not limited to any specific format in which physical storageis presented as logical storage (volume, LUNs and others)

Each storage volume may be configured to store data files (or datacontainers or data objects), scripts, word processing documents,executable programs, and any other type of structured or unstructureddata. From the perspective of one of the client systems, each storagevolume can appear to be a single drive. However, each storage volume canrepresent storage space in at one storage device, an aggregate of someor all of the storage space in multiple storage devices, a RAID group,or any other suitable set of storage space.

A storage volume is identified by a unique identifier (Volume-ID) and isallocated certain storage space during a configuration process. When thestorage volume is created, a QOS policy may be associated with thestorage volume such that requests associated with the storage volume canbe managed appropriately. The QOS policy may be a part of a QOS policygroup (referred to as “Policy Group”) that is used to manage QOS forseveral different storage volumes as a single unit. The QOS policyinformation may be stored at a QOS data structure 111 maintained by aQOS module 109. QOS at the storage system level may be implemented bythe QOS module 109. QOS module 109 maintains various QOS data types thatare monitored and analyzed by the performance manager 121, as describedbelow in detail.

The storage operating system 107 organizes physical storage space atstorage devices 114 as one or more “aggregate”, where each aggregate isa logical grouping of physical storage identified by a unique identifierand a location. The aggregate includes a certain amount of storage spacethat can be expanded. Within each aggregate, one or more storage volumesare created whose size can be varied. A qtree, sub-volume unit may alsobe created within the storage volumes. For QOS management, eachaggregate and the storage devices within the aggregates are consideredas resources that are used by storage volumes.

The storage system 108 may be used to store and manage information atstorage devices 114 based on an I/O request. The request may be based onfile-based access protocols, for example, the Common Internet FileSystem (CIFS) protocol or Network File System (NFS) protocol, over theTransmission Control Protocol/Internet Protocol (TCP/IP). Alternatively,the request may use block-based access protocols, for example, the SmallComputer Systems Interface (SCSI) protocol encapsulated over TCP (iSCSI)and SCSI encapsulated over Fibre Channel (FCP).

In a typical mode of operation, a client (or a VM) transmits one or moreI/O request, such as a CFS or NFS read or write request, over aconnection system 110 to the storage system 108. Storage operatingsystem 107 receives the request, issues one or more I/O commands tostorage devices 114 to read or write the data on behalf of the clientsystem, and issues a CIFS or NFS response containing the requested dataover the network 110 to the respective client system.

System 100 may also include a virtual machine environment where aphysical resource is time-shared among a plurality of independentlyoperating processor executable VMs. Each VM may function as aself-contained platform, running its own operating system (OS) andcomputer executable, application software. The computer executableinstructions running in a VM may be collectively referred to herein as“guest software.” In addition, resources available within the VM may bereferred to herein as “guest resources.”

The guest software expects to operate as if it were running on adedicated computer rather than in a VM. That is, the guest softwareexpects to control various events and have access to hardware resourceson a physical computing system (may also be referred to as a hostplatform or host system) which may be referred to herein as “hosthardware resources”. The host hardware resource may include one or moreprocessors, resources resident on the processors (e.g., controlregisters, caches and others), memory (instructions residing in memory,e.g., descriptor tables), and other resources (e.g., input/outputdevices, host attached storage, network attached storage or other likestorage) that reside in a physical machine or are coupled to the hostsystem.

In one aspect, system 100 may include a plurality of computing systems102A-102N (may also be referred to individually as host platform/system102 or simply as server 102) communicably coupled to the storage system108 via the connection system 110 such as a local area network (LAN),wide area network (WAN), the Internet or any other interconnect type. Asdescribed herein, the term “communicably coupled” may refer to a directconnection, a network connection, a wireless connection or otherconnections to enable communication between devices.

Host system 102A includes a processor executable virtual machineenvironment having a plurality of VMs 105A-105N that may be presented toclient computing devices/systems 116A-116N. VMs 105A-105N execute aplurality of guest OS 104A-104N (may also be referred to as guest OS104) that share hardware resources 120. As described above, hardwareresources 120 may include processors, memory, I/O devices, storage orany other hardware resource.

In one aspect, host system 102 interfaces with a virtual machine monitor(VMM) 106, for example, a processor executed Hyper-V layer provided byMicrosoft Corporation of Redmond, Wash., a hypervisor layer provided byVMWare Inc., or any other type. VMM 106 presents and manages theplurality of guest OS 104A-104N executed by the host system 102. The VMM106 may include or interface with a virtualization layer (VIL) 123 thatprovides one or more virtualized hardware resource to each OS 104A-104N.

In one aspect, VMM 106 is executed by host system 102A with VMs105A-105N. In another aspect, VMM 106 may be executed by an independentstand-alone computing system, often referred to as a hypervisor serveror VMM server and VMs 105A-105N are presented at one or more computingsystems.

It is noteworthy that different vendors provide different virtualizationenvironments, for example, VMware Corporation, Microsoft Corporation andothers. The generic virtualization environment described above withrespect to FIG. 1 may be customized to implement the aspects of thepresent disclosure. Furthermore, VMM 106 (or VIL 123) may execute othermodules, for example, a storage driver, network interface and others,the details of which are not germane to the aspects described herein andhence have not been described in detail.

System 100 may also include a management console 118 that executes aprocessor executable management application 117 for managing andconfiguring various elements of system 100. Application 117 may be usedto manage and configure VMs and clients as well as configure resourcesthat are used by VMs/clients, according to one aspect. It is noteworthythat although a single management console 118 is shown in FIG. 1, system100 may include other management consoles performing certain functions,for example, managing storage systems, managing network connections andother functions described below.

In one aspect, application 117 may be used to present storage space thatis managed by storage system 108 to clients' 116A-116N (or VMs). Theclients may be grouped into different service levels (also referred toas SLOs), where a client with a higher service level may be providedwith more storage space than a client with a lower service level. Aclient at a higher level may also be provided with a certain QOSvis-à-vis a client at a lower level.

Although storage system 108 is shown as a stand-alone system, i.e. anon-cluster based system, in another aspect, storage system 108 may havea distributed architecture; for example, a cluster based system of FIG.2A. Before describing the various aspects of the performance manager121, the following provides a description of a cluster based storagesystem.

Clustered Storage System: FIG. 2A shows a cluster based storageenvironment 200 having a plurality of nodes for managing storagedevices, according to one aspect. Storage environment 200 may include aplurality of client systems 204.1-204.N (similar to clients 116A-116N,FIG. 1), a clustered storage system 202, performance manager 121,management console 118 and at least a network 206 communicablyconnecting the client systems 204.1-204.N and the clustered storagesystem 202.

The clustered storage system 202 includes a plurality of nodes208.1-208.3, a cluster switching fabric 210, and a plurality of massstorage devices 212.1-212.3 (may be referred to as 212 and similar tostorage device 114) that are used as resources for processing I/Orequests.

Each of the plurality of nodes 208.1-208.3 is configured to include anetwork module (maybe referred to as N-module), a storage module (maybereferred to as D-module), and a management module (maybe referred to asM-Module), each of which can be implemented as a processor executablemodule. Specifically, node 208.1 includes a network module 214.1, astorage module 216.1, and a management module 218.1, node 208.2 includesa network module 214.2, a storage module 216.2, and a management module218.2, and node 208.3 includes a network module 214.3, a storage module216.3, and a management module 218.3.

The network modules 214.1-214.3 include functionality that enable therespective nodes 208.1-208.3 to connect to one or more of the clientsystems 204.1-204.N over the computer network 206, while the storagemodules 216.1-216.3 connect to one or more of the storage devices212.1-212.3. Accordingly, each of the plurality of nodes 208.1-208.3 inthe clustered storage server arrangement provides the functionality of astorage server.

The management modules 218.1-218.3 provide management functions for theclustered storage system 202. The management modules 218.1-218.3 collectstorage information regarding storage devices 212.

Each node may execute or interface with a QOS module, shown as109.1-109.3 that is similar to the QOS module 109. The QOS module 109may be executed for each node or a single QOS module may be used for theentire cluster. The aspects disclosed herein are not limited to thenumber of instances of QOS module 109 that may be used in a cluster.

A switched virtualization layer including a plurality of virtualinterfaces (VIFs) 201 is provided to interface between the respectivenetwork modules 214.1-214.3 and the client systems 204.1-204.N, allowingstorage 212.1-212.3 associated with the nodes 208.1-208.3 to bepresented to the client systems 204.1-204.N as a single shared storagepool.

The clustered storage system 202 can be organized into any suitablenumber of virtual servers (also referred to as “vservers” or storagevirtual machines (SVM)), in which each SVM represents a single storagesystem namespace with separate network access. Each SVM has a clientdomain and a security domain that are separate from the client andsecurity domains of other SVMs. Moreover, each SVM is associated withone or more VIFs and can span one or more physical nodes, each of whichcan hold one or more VIFs and storage associated with one or more SVMs.Client systems can access the data on a SVM from any node of theclustered system, through the VIFs associated with that SVM. It isnoteworthy that the aspects described herein are not limited to the useof SVMs.

Each of the nodes 208.1-208.3 is defined as a computing system toprovide application services to one or more of the client systems204.1-204.N. The nodes 208.1-208.3 are interconnected by the switchingfabric 210, which, for example, may be embodied as a Gigabit Ethernetswitch or any other type of switching/connecting device.

Although FIG. 2A depicts an equal number (i.e., 3) of the networkmodules 214.1-214.3, the storage modules 216.1-216.3, and the managementmodules 218.1-218.3, any other suitable number of network modules,storage modules, and management modules may be provided. There may alsobe different numbers of network modules, storage modules, and/ormanagement modules within the clustered storage system 202. For example,in alternative aspects, the clustered storage system 202 may include aplurality of network modules and a plurality of storage modulesinterconnected in a configuration that does not reflect a one-to-onecorrespondence between the network modules and storage modules.

Each client system 204.1-204.N may request the services of one of therespective nodes 208.1, 208.2, 208.3, and that node may return theresults of the services requested by the client system by exchangingpackets over the computer network 206, which may be wire-based, opticalfiber, wireless, or any other suitable combination thereof.

Performance manager 121 interfaces with the various nodes and obtainsQOS data for QOS data structure 125. Details regarding the variousmodules of performance manager are now described with respect to FIG.2B.

Performance Manager 121: FIG. 2B shows a block diagram of a system 200Awith details regarding performance manager 121 and a collection module211, according to one aspect. Performance manager 121 may include orinterface with a SLO management module 281 that includes a SLO monitor281A and a SLO scheduler 281B. The SLO monitor 281A tracks overall SLOallotments as described below in detail. The SLO scheduler 281B is usedto dynamically rebalance SLO allotments, as described below in detail.

Performance manager 121 uses the concept of workloads for trackingresources and workloads in a networked storage environment. At a highlevel, workloads are defined based on incoming I/O requests and useresources within storage system 202 for processing I/O requests. Aworkload may include a plurality of streams, where each stream includesone or more requests. A stream may include requests from one or moreclients. An example, of the workload model used by performance manager121 is shown in FIG. 2F and described below in detail.

Performance manager 121 collects a certain amount of data (for example,data for 3 hours or 30 data samples) of workload activity. Aftercollecting the QOS data, performance manager 121 determines the headroomfor a resource. Performance manager 121 uses the headroom to representavailable performance resource capacity at any given time. The availableheadroom information may be stored at a storage device as part of SLOmanagement data structure 125A.

Performance 121 includes a headroom module 221 that includes a pluralityof sub-modules including a filtering module 237, an optimal point module225 and an analysis module 223. The filtering module 237 filterscollected QOS data (shown as incoming data 229) and provides thefiltered data to the optimal point module 225. The optimal point module225 then determines an optimal point for a latency v utilization (LvU)curve shown in FIG. 6C. In one aspect, the optimal point module 225determines the optimal point using a plurality of techniques.

The optimal point with the LvU curve is provided to the analysis module223 that uses the curve and determines the headroom based on one or moreoperational points.

In one aspect, the headroom module 221 and components may be implementedas a processor executable, application programming interface (API) whichprovides a set of routines, protocols, and tools for building aprocessor executable software application that can be executed by acomputing device. When the headroom module 221 is implemented as one ormore APIs, then. it provides software components' terms of itsoperations, inputs, outputs, and underlying types. The APIs may beimplemented as plug-in APIs which integrate headroom computation andanalysis with. other management applications.

System 200A further shows two clusters 202A and 202B, both similar tocluster 202 described above. Each cluster includes the QOS module 109for implementing QOS policies and appropriate counters for collectinginformation regarding various resources. Cluster 1 202A may beaccessible to clients 204.1 and 204.2, while cluster 2 202B isaccessible to clients 204.3/204.4. Both clusters have access to storagesubsystems 207 and storage devices 212.1/212.N.

Clusters 202A and 202B communicate with collection module 211. Thecollection module 211 may be a standalone computing device or integratedwith performance manager 121. The aspects described herein are notlimited to any particular configuration of collection module 211 andperformance manager 121.

Collection module 211 includes one or more acquisition modules 219 forcollecting QOS data from the clusters. The data is pre-processed by thepre-processing module 215 and stored as pre-processed QOS data 217 at astorage device (not shown). Pre-processing module 215 formats thecollected QOS data for the performance manager 121. Pre-processed QOSdata 217 is provided to a collection module interface 231 of theperformance manager 121 via the performance manager interface 213. QOSdata received from collection module 211 is stored as QOS data structure125 (shown as incoming data 229) and used by the filtering module 237,before the data is used for computing the optimal point.

In one aspect, the performance manager 121 includes a GUI 229. Client205 may access the SLO management module 281 using the GUI module 229.The client may assign a SLO to one or more workloads. Once the workloadis identified, the performance manager 121 stores the historicalperformance for the workload and the performance capacity of theresources that are used by the workload. In one aspect, the SLO monitor281A generates a historical view that depicts the combined throughput(or IOPS) of all workloads that are associated with an SLO over a giventime. The historical view presents the SLO throughput requirement alongwith the headroom for the resources of the networked storageenvironment.

The historical view provides durations when a given SLO allotment is notbeing utilized. Using the historical view, a schedule can be created bythe SLO scheduler 281B that enforces SLO requirements during non-idletimes and deactivates or modifies the SLO enforcement during idle times.

Once a SLO schedule is created, then the storage cluster tracks thenon-idle and idle times for a given SLO. If the schedule does not matchwith actual idle times and non-idle times, then the user is notified.

In one aspect, the SLO management module 281 machine learns the highsand lows of workload demands. The SLO management module 281 providesguidance with regard to how long to schedule periodic tasks andworkloads. The historical patterns are learned using state based modelsdescribed below with respect to FIG. 6B.

Before describing the various processes involving performance manager121 and its components, the following provides an overview of QOS ingeneral, as used by the various aspects of the present disclosure.

QOS Overview: FIG. 2C shows a network module 214 of a cluster nodeincludes a network interface 214A for receiving requests from clients.Network module 214 executes a NFS module 214C for handling NFS requests,a CIFS module 214D for handling CIFS requests, a SCSI module 214E forhandling iSCSI requests and an others module 214F for handling “other”requests. A node interface 214G is used to communicate with QOS module109, storage module 216 and/or another network module 214. QOSmanagement interface 214B is used to provide QOS data from the clusterto collection module 211 for pre-processing data.

QOS module 109 includes a QOS controller 109A, a QOS request classifier109B and QOS policy data structure (or

Policy Group) 111. The QOS policy data structure 111 stores policy leveldetails for implementing QOS for clients and storage volumes. The policydetermines what latency and throughput rate is permitted for aclient/SLO as well as for specific storage volumes. The policydetermines how I/O requests are processed for different volumes andclients.

The storage module 216 executes a file system 216A (a part of storageoperating system 107 described below) and includes a storage layer 216Bto interface with storage device 212.

NVRAM 216C of the storage module 216 may be used as a cache forresponding to I/O requests. In one aspect, for executing a writerequest, the write data associated with the write request is firststored at a memory buffer of the storage module 216. The storage module216 acknowledges that the write request is completed after it is storedat the memory buffer. The data is then moved from the memory buffer tothe NVRAM 216C and then flushed to the storage device 212, referred toas consistency point (CP).

An I/O request arrives at network module 214 from a client or from aninternal process directly to file system 216A. Internal process in thiscontext may include a de-duplication module, a replication engine moduleor any other entity that needs to perform a read and/or write operationat the storage device 212. The request is sent to the QOS requestclassifier 109B to associate the request with a particular workload. Theclassifier 109B evaluates a request's attributes and looks for matcheswithin QOS policy data structure 111. The request is assigned to aparticular workload, when there is a match. If there is no match, then adefault workload may be assigned.

Once the request is classified for a workload, then the requestprocessing can be controlled. QOS controller 109A determines if a ratelimit (i.e. a throughput rate) for the request has been reached. If yes,then the request is queued for later processing. If not, then therequest is sent to file system 216A for further processing with acompletion deadline. The completion deadline is tagged with a messagefor the request.

File system 216A determines how queued requests should be processedbased on completion deadlines. The last stage of QOS control forprocessing the request occurs at the physical storage device level. Thiscould be based on latency with respect to storage device 212 or overallnode capacity/utilization as described below in detail.

Performance Model: FIG. 2D shows an example of a queuing structure usedby the performance manager 121 for SLO management and determiningheadroom, according to one aspect. A user workload enters the queuingnetwork from one end (i.e. at 233) and leaves at the other end.

Various resources are used to process I/O requests. As an example, thereare may be two types of resources, a service center and a delay centerresource. The service center is a resource category that can berepresented by a queue with a wait time and a service time (for example,a processor that processes a request out of a queue). The delay centermay be a logical representation for a control point where a requeststalls waiting for a certain event to occur and hence the delay centerrepresents the delay in request processing. The delay center may berepresented by a queue that does not include service time and insteadonly represents wait time. The distinction between the two resourcetypes is that for a service center, the QOS data includes a number ofvisits, wait time per visit and service time per visit for incidentdetection and analysis. For the delay center, only the number of visitsand the wait time per visit at the delay center are used, as describedbelow in detail.

Performance manager 121 uses different flow types for its analysis. Aflow type is a logical view for modeling request processing from aparticular viewpoint. The flow types include two categories, latency andutilization. A latency flow type is used for analyzing how longoperations take at the service and delay centers. The latency flow typeis used to identify a workload whose latency has increased beyond acertain level. A typical latency flow may involve writing data to astorage device based on a client request and there is latency involvedin writing the data at the storage device. The utilization flow type isused to understand resource consumption of workloads and may be used toidentify resource contention.

Referring now to FIG. 2D, delay center network 235 is a resource queuethat is used to track wait time due to external networks. Storageoperating system 107 often makes calls to external entities to wait onsomething before a request can proceed. Delay center 235 tracks thiswait time using a counter (not shown).

Network module delay center 237 is another resource queue where I/Orequests wait for protocol processing by a network module processor.This delay center 237 is used to track the utilization/capacity of thenetwork module 216. Overutilization of this resource may cause latency,as described below in detail.

NV_RAM transfer delay center 273 is used to track how the non-volatilememory may be used by cluster nodes to store write data before, the datais written to storage devices 212, in one aspect, as described below indetail.

A storage aggregate (or aggregate) 239 is a resource that may includemore than one storage device for reading and writing information.Aggregate 239 is tracked to determine if the aggregate is fragmentedand/or over utilized, as described below in detail.

Storage device delay center 241 may be used to track the utilization ofstorage devices 212. In one aspect, storage device utilization is basedon how busy a storage device may be in responding to I/O requests.

In one aspect, storage module delay center 245 is used for tracking nodeutilization. Delay center 245 is tracked to monitor the idle time for aCPU used by the storage module 216, the ratio of sequential and paralleloperations executed by the CPU and a ratio of write duration andflushing duration for using NVRAM 216C or an NVRAM at the storage module(not shown).

Nodes within a cluster communicate with each other. These may causedelays in processing I/O requests. The cluster interconnect delay center247 is used to track the wait time for transfers using the clusterinterconnect system. As an example, a single queue maybe used to trackdelays due to cluster interconnects.

There may also be delay centers due to certain internal processes ofstorage operating system 107 and various queues may be used to trackthose delays. For example, a queue may be used to track the wait for I/Orequests that may be blocked for file system reasons. Another queue(Delay_Center_Susp CP) may be used to represent the wait time forConsistency Point (CP) related to the file system 216A. During a CP,write requests are written in bulk at storage devices and this willtypically cause other write requests to be blocked so that certainbuffers are cleared.

Workload Model: FIG. 2E shows an example, of the workload model used byperformance manager 121, according to one aspect. As an example, aworkload may include a plurality of streams 251A-251N. Each stream mayhave a plurality of requests 253A-253N. The requests may be generated byany entity, for example, an external entity 255, like a client systemand/or an internal entity 257, for example, a replication engine thatreplicates storage volumes at one or more storage location.

A request may have a plurality of attributes, for example, a source, apath, a destination and I/O properties. The source identifies the sourcefrom where a request originates, for example, an internal process, ahost or client address, a user application and others.

The path defines the entry path into the storage system. For example, apath may be a logical interface (LIF) or a protocol, such as NFS, CIFS,iSCSI and Fibre Channel protocol. A destination is the target of arequest, for example, storage volumes, LUNs, data containers and others.I/O properties include operation type (i.e. read/write/other), requestsize and any other property.

In one aspect, streams may be grouped together based on client needs.For example, if a group of clients make up a department on two differentsubnets, then two different streams with the “source” restrictions canbe defined and grouped within the same workload. Furthermore, requeststhat fall into a workload are tracked together by performance 121 forefficiency. Any requests that don't match a user or system definedworkload may be assigned to a default workload.

In one aspect, workload streams may be defined based on the I/Oattributes. The attributes may be defined by clients. Based on thestream definition, performance manager 121 tracks workloads, asdescribed below.

Referring back to FIG. 2E, a workload uses one or more resources forprocessing I/O requests shown as 271A-271N as part of a resource object259. The resources include service centers and delay centers that havebeen described above with respect to FIG. 2D. For each resource, acounter/queue is maintained for tracking different statistics (or QOSdata) 261. For example, a response time 263, and a number of visits 265,a service time (for service centers) 267, a wait time 269 andinter-arrival time 275 are tracked. Inter-arrival time 275 is used totrack when an I/O request for reading or writing data is received at aresource. The term QOS data as used throughout this specificationincludes one or more of 263, 265, 267 and 269 according to one aspect.

Performance manager 121 may use a plurality of counter objects forresource monitoring and headroom analysis, according to one aspect.Without limiting the various adaptive aspects, an example of the variouscounter objects are shown and described in Table I below:

TABLE I Workload Object Counters Description OPS A number of workloadoperations that are completed during a measurement interval, forexample, a second. Read_ops A number of workload read operations thatare completed during the measurement interval. Write_ops A number ofworkload write operations that are completed during the measurementinterval. Total_data Total data read and written per second by aworkload. Read_data The data read per second by a workload. Write_dataThe data written per second by a workload. Latency The average responsetime for I/O requests that were initiated by a workload. Read_latencyThe average response time for read requests that were initiated by aworkload. Write_latency The average response time for write requeststhat were initiated by a workload. Classified Requests that wereclassified as part of a workload. Read_IO_type The percentage of readsserved from various components (for example, buffer cache, ext_cache ordisk). Concurrency Average number of concurrent requests for a workload.Interarrival_time_sum_squares Sum of the squares of the Inter-arrivaltime for requests of a workload.

Without limiting the various aspects of the present disclosure, Table IIbelow provides an example of the details associated with the objectcounters that are monitored by the performance manager 121, according toone aspect:

TABLE II Workload Detail Object Counter Description Visits A number ofvisits to a physical resource per second; this value is grouped by aservice center. Service_Time A workload's average service time per visitto the service center. Wait_Time A workload's average wait time pervisit to the service center.

Object Hierarchy: FIG. 3A shows an example of a format 300 for trackinginformation regarding different resources that are used within aclustered storage system (for example, 202, FIG. 2A). Each resource isidentified by a unique resource identifier value that is maintained bythe performance manager 121. The resource identifier value may be usedto obtain available performance capacity (headroom) of a resource.

Format 300 maybe hierarchical in nature where various objects may haveparent-child, peer and remote peer relationships, as described below. Asan example, format 300 shows a cluster object 302 that may becategorized as a root object type for tracking cluster level resources.The cluster object 302 is associated with various child objects, forexample, a node object 306, QOS network object 304, a portset object318, a SVM object 324 and a policy group 326. The cluster object 302stores information regarding the cluster, for example, the number ofnodes it may have, information identifying the nodes; and any otherinformation.

The QOS network object 304 is used to monitor network resources, forexample, network switches and associated bandwidth used by a clusteredstorage system.

The cluster node object 306 stores information regarding a node, forexample, a node identifier and other information. Each cluster nodeobject 306 is associated with a pluralities of child objects, forexample, a cache object 308, a QOS object for a storage module 310, aQOS object for a network module 314, a CPU object 312 and an aggregateobject 316. The cache object 308 is used to track utilization/latency ofa cache (for example, NVRAM 216C, FIG. 2D). The QOS storage module 310tracks the QOS of a storage module defined by a QOS policy datastructure 111 described above in detail with respect to FIG. 2D. The QOSnetwork module object 314 tracks the QOS for a network module. The CPUobject 312 is used to track CPU performance and utilization of a node.

The aggregate object 316 tracks the utilization/latency of a storageaggregate that is managed by a cluster node. The aggregate object mayhave various child objects, for example, a flash pool object 332 thattracks usage of a plurality of flash based storage devices (shown as“flash pool”). The flash pool object 332 may have a SSD disk object 336that tracks the actual usage of specific SSD based storage devices. TheRAID group 334 is used to track the usage of storage devices configuredas RAID devices. The RAID object 334 includes a storage device object338 (shown as a HDD (hard disk drive) that tracks the actual utilizationof the storage devices.

Each cluster is provided a portset having a plurality of ports that maybe used to access cluster resources. A port includes logic and circuitryfor processing information that is used for communication betweendifferent resources of the storage system. The portset object 318 tracksthe various members of the portset using a port object 320 and a LIFobject 322. The LIF object 322 includes a logical interface, forexample, an IP address, while the port object 320 includes a portidentifier for a port, for example, a world-wide port number (WWPN). Itis noteworthy that the port object 320 is also a child object of node306 that may use a port for network communication with clients.

A cluster may present one or more SVMs to client systems. The SVMs aretracked by the SVM object 324, which is a child object of cluster 302.Each cluster is also associated with a policy group that is tracked by apolicy group object 326. The policy group 326 is associated with SVMobject 324 as well as storage volumes and LUNs. The storage volume istracked by a volume object 328 and the LUN is tracked by a LUN object330. The volume object 328 includes an identifier identifying a volume,size of the volume, clients associated with the volume, volume type(i.e. flexible or fixed size) and other information. The LUN object 330includes information that identifies the LUN (LUNID), size of the LUN,LUN type (read, write or read and write) and other information.

FIG. 3B shows an example of some additional counters that are used forheadroom analysis, described below in detail. These counters are relatedto nodes and aggregates and are in addition to the counters of Table Idescribed above. For example, counter 306A is used to track theutilization i.e. idle time for each node processor. Node latency counter306B tracks the latency at nodes based on operation types. The latencymay be based on the total number of visits at a storage systemnode/number of operations per second for a workload. This value may notinclude internal or system default workloads, as described below indetail.

Aggregate utilization is tracked using counter 316A that tracks theduration of how busy a device may be for processing user requests. Anaggregate latency counter 316B tracks the latency due to the storagedevices within an aggregate. The latency may be based on a measureddelay for each storage device in an aggregate. The use of these countersfor headroom analysis is described below in detail.

Process Flow: FIG. 4 shows a process flow 400 for SLO management,according to one aspect of the present disclosure. The process begins inblock B402 when the performance manager 121 and the storage clusternodes are initialized and operational.

In block B404, a user interface is presented to a client system. Theuser interface may be provided by the GUI module 229.

In block B406, a user applies SLOs to several volumes (or workloads).The SLOs establish a guaranteed service level for the volumes. Once theSLOs are applied, the information regarding the SLO requirements arestored at a policy data structure. The requirements provide metrics forthe clustered storage system to process user requests within theparameters of a specific SLO. The SLOs requirements may have defaultvalues and/or may be configurable.

Once the SLOs are defined, the user starts using the volumes to storedata. In one aspect, in block B408, using QOS data, the SLO monitor 281Atracks volume performance data. The performance capacity of theresources used by the volume are also tracked. The SLO monitor 281Areviews the usage pattern of each SLO to determine when the SLOallocation is being under-utilized.

In block B412, the process determines if dynamic SLO adjustment isenabled based on a load (for example, IOPS) that the workload is sendingto the resources. This may be determined by checking volumeconfiguration data. When a volume is configured, an attribute may bedefined for dynamic SLO adjustment. If not, then in block B414, the useris notified of the usage pattern for each SLO and the user may make thenecessary changes for the SLOs, based on the usage pattern.

If dynamic adjustment is enabled, then in block B416, the SLO monitor281A adjusts the latency target for each SLO based on the historicalpattern. The SLO monitor 281A determines idle periods for each SLO basedon certain thresholds. The adjustments to SLO throughput are made byincreasing the target latency. Because the latency targets are adjusted,it provides additional performance capacity for the resources. The SLOmonitor 281A may interface with the analysis module 223 to ascertain theadditional performance capacity.

In block B420, the process determines if dynamic SLO re-balancing isenabled. This may be enabled as part of a policy, a client attribute, avolume attribute or any other factor. If the dynamic SLO balancing isnot enabled, then in block B422, the user is notified and the user canreclaim the performance capacity and use the resources for otherworkloads.

If in block B420, the dynamic balancing is enabled, then in block B424,the SLO monitor 281A identifies a SLO candidate that may need anincrease in their allotment i.e. increase in required SLO throughput.This may be based on real time SLO monitoring. The process thenautomatically adjusts the SLO allotments, The SLOs and the respectiveworkloads are monitored in block B426.

FIG. 5 depicts a layout 500 for executing the process blocks of FIG. 4.Layout 500 shows a plurality of SLOs 502A-502N that are assigned to aplurality of volumes 504A-504N. The volume performance data and theavailable resource capacity is maintained in data structures 506A-506Nand 508A-508N. These data structures are part of the SLO management datastructure 125A described above.

The volume performance data 506A provides an overview of the overallvolume i.e. throughput and latency based on collected QOS data. The datastructure 508A-508N show the available headroom of the resources used byvolumes 504A-504N. Using these data structures enable the SLO managementmodule 281 to execute the process blocks of FIG. 4, described above indetail.

FIG. 6A shows a graphical illustration 600 generated by the processblocks of FIG. 4, according to one aspect of the present disclosure. TheSLO throughput allotment is shown as 606. The throughput allotment showsthe maximum number of IOPS or throughput for a specific SLO. Theresource performance capacity limit for each resource used by a volumeis shown as 604. The historical pattern shows idle times as 610 and SLOutilization by the triangles 608. The graphical illustration enablesdeactivation of the SLO during idle times 610 or create a modified SLOfor that duration. This provides additional performance resourcecapacity that may be used to increase another SLO allotment.

FIG. 6B shows an example of a state-based model 612 executed by the SLOmanagement module 281. In the model 612, the length of time in lowdemand 614 and high demand 616 provides the periodicity of “idleness” ina resource or workload. This periodicity and the respective time in eachstate is used by the SLO scheduler 281B to determine automatic SLOadjustment allotment. In one aspect, the automatic adjustment schedulesare very efficient for example, in a cloud environment where thousandsof SLOs and volumes are used by numerous clients.

Headroom Computation and Analysis: The performance capacity of aresource is determined by the headroom module 221, a processorexecutable module. The headroom module 221 uses performance data (forexample, latency and utilization data, inter-arrival times and/orservice times) for at least the cluster nodes and aggregates has beencollected. The collected data is provided to the performance manager121. In one aspect, current and historical QOS data may both be accessedby the performance manager 121 for determining headroom. The performancemanager 121 also obtains information regarding any events that may haveoccurred at the storage system level associated with the QOS data. Anypolicy information that is associated with the resource for which theQOS data is also obtained by the performance manager 121.

The filtering module 237 filters the collected data. In one aspect,potential erroneous observations such as unreasonable large latencyvalues, variances, service times or utilizations are identified. Ifthere is any data associated with unusual events like hardware failureor network failure that may affect performance may be discarded. Forexample, if a flash memory card used by a node fails and has to bereplaced, then the latency for processing I/O requests with the failedcard may be unreasonably high and hence data associated with that nodemay not be reliable for headroom computations. Any outliers in thecollected and historical QOS data may also be removed (for example, thetop 5-10% and the bottom 5-10% of the latency and utilization values maybe discarded).

In one aspect, filtering module 237 may also insert missing data,according to one aspect. For example, service times for differentresources are expected to be within a range based on collectedhistorical service time data. If the collected data have a highcoefficient of variation, then the collected data may not be reliableand hence may have to be corrected.

After the data is filtered, one or more LvU curves are generated and anoptimal point is determined by the optimal point module 225. In oneaspect, as an example, different techniques (for example, model basedand observation based techniques) are used to generate the LvU curvesand compute the optimal point. The technique that provides the mostreliable optimal point is used for headroom analysis.

The model based technique uses current observations and queueing modelsto generate the LvU curve. The model based technique uses inter-arrivaltimes and service times for a resource. The inter-arrival times trackthe arrival times for I/O requests at a resource, while the servicetimes track the duration for servicing user based I/O requests. Theobservation based technique uses both current and historicalobservations of latency and utilizations for generating LvU curves. Itis noteworthy that the various adaptive aspects of the presentdisclosure are not limited to any specific technique.

The optimal point is selected and provided to the analysis module 223.The optimal point may be based on a policy based input, for example, aSLO input (for example, from a policy). The SLO input defines a latencylimit that is assigned for a user/resource. The analysis module 223determines the headroom (using the optimal point and an operationalpoint. In one aspect, different operational points may be used for aresource based on the operating environment and how the resources arebeing used. For example, a current total utilization may be used as anoperational point with the presumption that the current totalutilization may be used to process a workload mix.

In another aspect, a custom operational point may be used when a volumeis identified in a policy. In another aspect, the analysis module 223may ascertain the effect of moving workloads which may affectutilization and the operational point. In yet another aspect, theutilization of a node pair that are configured as high availability (HA)pair nodes is considered for the operational point. When nodes operateas HA pair nodes and if one of the nodes becomes unavailable, then theother node takes over workload processing. In this instance,latency/utilization of both the nodes is used for determining theoperational point and computing the headroom. This headroom analysis isreferred to as the actual headroom. Headroom information may be storedat data structure 125A.

Latency v Utilization Curve: In one aspect, the remaining or availableperformance capacity is determined from a LvU curve. FIG. 6C shows anexample of a relationship 628 between latency and utilization of aresource to determine headroom or performance capacity of a resource atany given time. Latency 618 for a given resource that is used to processworkloads is shown on the vertical, Y-axis, while the utilization 622 ofthe resource is shown on the X-axis.

The latency v utilization curve shows an optimal point 624, after whichlatency shows a rapid increase. Optimal point represents maximumutilization of a resource beyond which an increase in workload areassociated with higher throughput gains than latency increase. Beyondthe optimal point, if the workload increases at a resource, thethroughput gains or utilization increase is smaller than the increase inlatency. An optimal point may be determined by a plurality of techniquesdefined below. The optimal point may also be customized based on a SLOthat guarantees certain latency/utilization for a user.

An operational point 620 shows current utilization of the resource. Theavailable performance capacity is shown as 626. In one aspect, theoperational point 620 may be determined based on current utilization ofa resource. The operational point may also be determined based on theeffect of internal workloads (for example, when a storage volume ismoved), when a storage node is configured as a high availabilityfailover nodes or when there are workloads that can be throttled ordelayed because they may not be very critical.

In one aspect, headroom (or performance capacity) may be based on thefollowing relationship:

${Headroom} = \frac{{{Optimal}\mspace{14mu} {Point}} - {{Operational}\mspace{14mu} {Point}}}{{Optimal}\mspace{14mu} {Point}}$

Details of generating a LvU curve are provided in the U.S. patentapplication, Ser. No. 14/994,009 (docket number P01-010798.01.US.PRI)filed on Jan. 12, 2016, the disclosure of which is incorporated hereinby reference in its entirety.

Storage System Node: FIG. 7 is a block diagram of a node 208.1 that isillustratively embodied as a storage system comprising of a plurality ofprocessors 702A and 702B, a memory 704, a network adapter 710, a clusteraccess adapter 712, a storage adapter 716 and local storage 713interconnected by a system bus 708. Node 208.1 is used as a resource andmay be used to provide node and storage utilization information toperformance manager 121 described above in detail.

Processors 702A-702B may be, or may include, one or more programmablegeneral-purpose or special-purpose microprocessors, digital signalprocessors (DSPs), programmable controllers, application specificintegrated circuits (ASICs), programmable logic devices (PLDs), or thelike, or a combination of such hardware devices. Idle time forprocessors 702A-702A is tracked by counters 306A, described above indetail.

The local storage 713 comprises one or more storage devices utilized bythe node to locally store configuration information for example, in aconfiguration data structure 714. The configuration information mayinclude information regarding storage volumes and the QOS/SLO associatedwith each storage volume.

The cluster access adapter 712 comprises a plurality of ports adapted tocouple node 208.1 to other nodes of cluster 202. In the illustrativeaspect, Ethernet may be used as the clustering protocol and interconnectmedia, although it will be apparent to those skilled in the art thatother types of protocols and interconnects may be utilized within thecluster architecture described herein. In alternate aspects where thenetwork modules and storage modules are implemented on separate storagesystems or computers, the cluster access adapter 712 is utilized by thenetwork/storage module for communicating with othernetwork/storage-modules in the cluster 202.

Each node 208.1 is illustratively embodied as a dual processor storagesystem executing a storage operating system 706 (similar to 107, FIG. 1)that preferably implements a high-level module, such as a file system,to logically organize the information as a hierarchical structure ofnamed directories and files at storage 212.1. However, it will beapparent to those of ordinary skill in the art that the node 208.1 mayalternatively comprise a single or more than two processor systems.Illustratively, one processor 702A executes the functions of the networkmodule on the node, while the other processor 702B executes thefunctions of the storage module.

The memory 704 illustratively comprises storage locations that areaddressable by the processors and adapters for storing programmableinstructions and data structures. The processor and adapters may, inturn, comprise processing elements and/or logic circuitry configured toexecute the programmable instructions and manipulate the datastructures. It will be apparent to those skilled in the art that otherprocessing and memory means, including various computer readable media,may be used for storing and executing program instructions pertaining tothe disclosure described herein.

The storage operating system 706 portions of which is typically residentin memory and executed by the processing elements, functionallyorganizes the node 208.1 by, inter alia, invoking storage operation insupport of the storage service implemented by the node.

The network adapter 710 comprises a plurality of ports adapted to couplethe node 208.1 to one or more clients 204.1/204.N over point-to-pointlinks, wide area networks, virtual private networks implemented over apublic network (Internet) or a shared local area network. The networkadapter 710 thus may comprise the mechanical, electrical and signalingcircuitry needed to connect the node to the network. Each client204.1/204.N may communicate with the node over network 206 (FIG. 2A) byexchanging discrete frames or packets of data according to pre-definedprotocols, such as TCP/IP.

The storage adapter 716 cooperates with the storage operating system 706executing on the node 208.1 to access information requested by theclients. The information may be stored on any type of attached array ofwritable storage device media such as video tape, optical, DVD, magnetictape, bubble memory, electronic random access memory, micro-electromechanical and any other similar media adapted to store information,including data and parity information. However, as illustrativelydescribed herein, the information is preferably stored at storage device212.1. The storage adapter 716 comprises a plurality of ports havinginput/output (I/O) interface circuitry that couples to the storagedevices over an I/O interconnect arrangement, such as a conventionalhigh-performance, Fibre Channel link topology.

Operating System: FIG. 8 illustrates a generic example of storageoperating system 706 (or 107, FIG. 1) executed by node 208.1, accordingto one aspect of the present disclosure. The storage operating system706 interfaces with the QOS module 109 and the performance manager 121such that proper bandwidth and QOS policies are implemented at thestorage volume level. The storage operating system 706 may also maintaina plurality of counters for tracking node utilization and storage deviceutilization information. For example, counters 306A-306B and 316A-316Cmay also be maintained by the storage operating system 706 and counterinformation is provided to the performance manager 121. In anotheraspect, performance manager 121 maintains the counters and they areupdated based on information provided by the storage operating system706.

In one example, storage operating system 706 may include severalmodules, or “layers” executed by one or both of network module 214 andstorage module 216. These layers include a file system manager 800 thatkeeps track of a directory structure (hierarchy) of the data stored instorage devices and manages read/write operation, i.e. executesread/write operation on storage in response to client 204.1/204.Nrequests.

Storage operating system 706 may also include a protocol layer 802 andan associated network access layer 806, to allow node 208.1 tocommunicate over a network with other systems, such as clients204.1/204.N. Protocol layer 802 may implement one or more of varioushigher-level network protocols, such as NFS, CIFS, Hypertext TransferProtocol (HTTP), TCP/IP and others.

Network access layer 806 may include one or more drivers, whichimplement one or more lower-level protocols to communicate over thenetwork, such as Ethernet. Interactions between clients' and massstorage devices 212.1-212.3 (or 114) are illustrated schematically as apath, which illustrates the flow of data through storage operatingsystem 706.

The storage operating system 706 may also include a storage access layer804 and an associated storage driver layer 808 to allow storage module216 to communicate with a storage device. The storage access layer 804may implement a higher-level storage protocol, such as RAID (redundantarray of inexpensive disks), while the storage driver layer 808 mayimplement a lower-level storage device access protocol, such as FibreChannel or SCSI. The storage driver layer 808 may maintain various datastructures (not shown) for storing information regarding storage volume,aggregate and various storage devices.

As used herein, the term “storage operating system” generally refers tothe computer-executable code operable on a computer to perform a storagefunction that manages data access and may, in the case of a node 208.1,implement data access semantics of a general purpose operating system.The storage operating system can also be implemented as a microkernel,an application program operating over a general-purpose operatingsystem, such as UNIX® or Windows XP®, or as a general-purpose operatingsystem with configurable functionality, which is configured for storageapplications as described herein.

In addition, it will be understood to those skilled in the art that thedisclosure described herein may apply to any type of special-purpose(e.g., file server, filer or storage serving appliance) orgeneral-purpose computer, including a standalone computer or portionthereof, embodied as or including a storage system. Moreover, theteachings of this disclosure can be adapted to a variety of storagesystem architectures including, but not limited to, a network-attachedstorage environment, a storage area network and a storage devicedirectly-attached to a client or host computer. The term “storagesystem” should therefore be taken broadly to include such arrangementsin addition to any subsystems configured to perform a storage functionand associated with other equipment or systems. It should be noted thatwhile this description is written in terms of a write any where filesystem, the teachings of the present disclosure may be utilized with anysuitable file system, including a write in place file system.

Processing System: FIG. 9 is a high-level block diagram showing anexample of the architecture of a processing system 900 that may be usedaccording to one aspect. The processing system 900 can representperformance manager 121, host system 102, management console 118,clients 116, 204, or storage system 108. Note that certain standard andwell-known components which are not germane to the present aspects arenot shown in FIG. 9.

The processing system 900 includes one or more processor(s) 902 andmemory 904, coupled to a bus system 905. The bus system 905 shown inFIG. 9 is an abstraction that represents any one or more separatephysical buses and/or point-to-point connections, connected byappropriate bridges, adapters and/or controllers. The bus system 905,therefore, may include, for example, a system bus, a PeripheralComponent Interconnect (PCI) bus, a HyperTransport or industry standardarchitecture (ISA) bus, a small computer system interface (SCSI) bus, auniversal serial bus (USB), or an Institute of Electrical andElectronics Engineers (IEEE) standard 1394 bus (sometimes referred to as“Firewire”).

The processor(s) 902 are the central processing units (CPUs) of theprocessing system 900 and, thus, control its overall operation. Incertain aspects, the processors 902 accomplish this by executingsoftware stored in memory 904. A processor 902 may be, or may include,one or more programmable general-purpose or special-purposemicroprocessors, digital signal processors (DSPs), programmablecontrollers, application specific integrated circuits (ASICs),programmable logic devices (PLDs), or the like, or a combination of suchdevices.

Memory 904 represents any form of random access memory (RAM), read-onlymemory (ROM), flash memory, or the like, or a combination of suchdevices. Memory 904 includes the main memory of the processing system900. Instructions 906 implement the process steps of FIG. 4 and storedata structure 125A described above may reside in and executed byprocessors 902 from memory 904.

Also connected to the processors 902 through the bus system 905 are oneor more internal mass storage devices 910, and a network adapter 912.Internal mass storage devices 910 may be, or may include anyconventional medium for storing large volumes of data in a non-volatilemanner, such as one or more magnetic or optical based disks. The networkadapter 912 provides the processing system 900 with the ability tocommunicate with remote devices (e.g., storage servers) over a networkand may be, for example, an Ethernet adapter, a Fibre Channel adapter,or the like.

The processing system 900 also includes one or more input/output (I/O)devices 908 coupled to the bus system 905. The I/O devices 908 mayinclude, for example, a display device, a keyboard, a mouse, etc.

Cloud Computing: The system and techniques described above areapplicable and especially useful in the cloud computing environmentwhere storage is presented and shared across different platforms. Cloudcomputing means computing capability that provides an abstractionbetween the computing resource and its underlying technical architecture(e.g., servers, storage, networks), enabling convenient, on-demandnetwork access to a shared pool of configurable computing resources thatcan be rapidly provisioned and released with minimal management effortor service provider interaction. The term “cloud” is intended to referto a network, for example, the Internet and cloud computing allowsshared resources, for example, software and information to be available,on-demand, like a public utility.

Typical cloud computing providers deliver common business applicationsonline which are accessed from another web service or software like aweb browser, while the software and data are stored remotely on servers.The cloud computing architecture uses a layered approach for providingapplication services. A first layer is an application layer that isexecuted at client computers. In this example, the application allows aclient to access storage via a cloud.

After the application layer, is a cloud platform and cloudinfrastructure, followed by a “server” layer that includes hardware andcomputer software designed for cloud specific services. The storagesystems/performance manager described above can be a part of the serverlayer for providing storage services. Details regarding these layers arenot germane to the inventive aspects.

Thus, methods and apparatus for managing resources in a storageenvironment have been described. Note that references throughout thisspecification to “one aspect” or “an aspect” mean that a particularfeature, structure or characteristic described in connection with theaspect is included in at least one aspect of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an aspect” or “one aspect” or “an alternative aspect” invarious portions of this specification are not necessarily all referringto the same aspect. Furthermore, the particular features, structures orcharacteristics being referred to may be combined as suitable in one ormore aspects of the disclosure, as will be recognized by those ofordinary skill in the art.

While the present disclosure is described above with respect to what iscurrently considered its preferred aspects, it is to be understood thatthe disclosure is not limited to that described above. To the contrary,the disclosure is intended to cover various modifications and equivalentarrangements within the spirit and scope of the appended claims.

What is claimed is:
 1. A machine implemented method, comprising:assigning by a processor executable management module a service levelobjective (SLO) for a workload, where the SLO is allotted a plurality ofperformance parameters for tracking performance of the workload forstoring data in a networked storage environment; tracking historicalperformance of the workload by the management module to determine aduration when SLO allotment defined by the plurality of performanceparameters is being under-utilized; adjusting automatically the SLOallotment for the workload by the management module during the durationwhen the SLO allotment is under-utilized; and re-allocatingautomatically by the management module available performance capacity ofa resource used by the workload to another workload whose assigned SLOis not being under-utilized, where the available performance capacity isdetermined by using a relationship between latency and optimumutilization of the resource, where the optimum utilization is anindicator of resource utilization beyond which throughput gains aresmaller than increase in latency.
 2. The method of claim 1, wherein theresource is a storage system node for storing data and the workload is astorage volume.
 3. The method of claim 1, wherein the resource is anaggregate that includes at least a storage device for storing data andthe workload is a storage volume.
 4. The method of claim 1, wherein theautomatic SLO adjustment is configurable by an attribute of theworkload.
 5. The method of claim 1, wherein automatic re-allocation ofthe available performance capacity is configurable by an attribute ofthe workload.
 6. The method of claim 1, wherein the plurality ofparameters define a number of input/output (I/O) operations per second(IOPS) and a latency for processing I/O requests associated with theworkload.
 7. The method of claim 1, wherein the management module isimplemented as an application programming interface (API).
 8. Anon-transitory, machine readable storage medium having stored thereoninstructions for performing a method, comprising machine executable codewhich when executed by at least one machine, causes the machine to:assign by a processor executable management module a service levelobjective (SLO) for a workload, where the SLO is allotted a plurality ofperformance parameters for tracking performance of the workload forstoring data in a networked storage environment; track historicalperformance of the workload by the management module to determine aduration when SLO allotment defined by the plurality of performanceparameters is being under-utilized; adjust automatically the SLOallotment for the workload by the management module during the durationwhen the SLO allotment is under-utilized; and re-allocate automaticallyby the management module available performance capacity of a resourceused by the workload to another workload whose assigned SLO is not beingunder-utilized, where the available performance capacity is determinedby using a relationship between latency and optimum utilization of theresource, where the optimum utilization is an indicator of resourceutilization beyond which throughput gains are smaller than increase inlatency.
 9. The non-transitory, storage medium of claim 8, wherein theresource is a storage system node for storing data and the workload is astorage volume.
 10. The non-transitory, storage medium of claim 8,wherein the resource is an aggregate that includes at least a storagedevice for storing data and the workload is a storage volume.
 11. Thenon-transitory, storage medium of claim 8, wherein the automatic SLOadjustment is configurable by an attribute of the workload.
 12. Thenon-transitory, storage medium of claim 8, wherein automaticre-allocation of the available performance capacity is configurable byan attribute of the workload.
 13. The non-transitory, storage medium ofclaim 8, wherein the plurality of parameters define a number ofinput/output (I/O) operations per second (IOPS) and a latency forprocessing I/O requests associated with the workload.
 14. Thenon-transitory, storage medium of claim 8, wherein the management moduleis implemented as an application programming interface (API).
 15. Asystem comprising: a memory containing machine readable mediumcomprising machine executable code having stored thereon instructions;and a processor module coupled to the memory, the processor moduleconfigured to execute the machine executable code to: assign a servicelevel objective (SLO) for a workload, where the SLO is allotted aplurality of performance parameters for tracking performance of theworkload for storing data in a networked storage environment; trackhistorical performance of the workload to determine a duration when SLOallotment defined by the plurality of performance parameters is beingunder-utilized; adjust automatically the SLO allotment for the workloadduring the duration when the SLO allotment is under-utilized; andre-allocate automatically the available performance capacity of aresource used by the workload to another workload whose assigned SLO isnot being under-utilized, where the available performance capacity isdetermined by using a relationship between latency and optimumutilization of the resource, where the optimum utilization is anindicator of resource utilization beyond which throughput gains aresmaller than increase in latency.
 16. The system of claim 15, whereinthe resource is a storage system node for storing data and the workloadis a storage volume.
 17. The system of claim 15, wherein the resource isan aggregate that includes at least a storage device for storing dataand the workload is a storage volume.
 18. The system of claim 15,wherein the automatic SLO adjustment is configurable by an attribute ofthe workload.
 19. The system of claim 15, wherein automaticre-allocation of the available performance capacity is configurable byan attribute of the workload.
 20. The system of claim 15, wherein theplurality of parameters define a number of input/output (I/O) operationsper second (IOPS) and a latency for processing I/O requests associatedwith the workload.